Using access log data to predict failure-prone students in Moodle using a small dataset

Abstract

In this paper, the authors present a predictive model for failure-prone students using access log data from two small datasets in the Moodle learning system. Although various advanced machine learning algorithms, especially supervised predictive methods, can be used with very large datasets, these tools are often not available for most initial university research, especially in developing countries, to predict learners’ future outcomes. The authors examined the use of students’ access patterns to track failure-prone students so that early interventions could be made to prevent failure or dropout. Real data were collected through explicit learners’ actions, such as completing assignments and taking quizzes, from two compulsory blended courses, Operating System (junior level, or third year) and System Analysis and Design (sophomore level, or second year). Research methods were predominantly quantitative. The proposed models correctly predicted failure-prone students before the end of the second academic month (fifth week) for both courses (88% of the class for juniors and 86% of the class for sophomores), which made it possible to intervene early and provide required support during the semester. Similarly, the study outcomes showed the students’ past performance, specifically their grade point average, could affect their final performance. The outcomes of this study can be used to analyze the behaviors of learners that lead to high success and high retention rate. Furthermore, the study results will be used to report and provide feedback to the educational parties to value the quality of teaching and learning, the improvement of course materials, and increasing learner success.